AWS Smart Business Blog

How Small and Medium Businesses Can Develop a Modern Data Strategy

In the era of big data, small and medium-sized businesses (SMBs) often find themselves wrestling with a deluge of data from an ever-growing range of sources. According to Gartner, 60 percent of organizations do not measure the costs of poor data quality. A lack of measurement results in reactive responses to data quality issues, missed business growth opportunities, and increased risks. The ability to harness this data effectively is becoming a crucial determinant of a company’s success. However, turning raw data into valuable insights requires a sound data strategy, efficient management, and modern data architecture. This blog will explore these aspects, with a particular focus on how Amazon Web Services can provide powerful solutions.

Exploring modern data challenges in SMBs

As data volumes swell and sources diversify, SMBs are frequently confronted by a series of data-related challenges. These challenges include:

Where to begin

A vision and a clear understanding of what a future-state architecture looks like is the beginning of the data architecture journey, and this is the responsibility of the executive sponsor of the data strategy. Not everything should be done at once, instead, prioritization of the area(s) of focus is essential for success. For example, AWS SMB customer Qure4u started out with Amazon QuickSight to create real-time data visualization dashboards for healthcare providers. Over time, this focus on a business data strategy led to 5x growth. “As a small or medium-size business, you can get off the ground quickly by using AWS. If you don’t have certain capabilities on your own team, AWS has the resources to connect you with specialists so that you can receive a professional setup at a low cost,” said Dr. Monica Bolbjerg, MD, CEO and founder of Qure4u.

Lack of effective strategy

Many businesses struggle to align their data strategy with their business vision. This challenge stems from the difficulty of translating business objectives into measurable data points and managing the data effectively. Execution of a data strategy involves people, process, and selection of the destination technologies with solutions when charting the course to a modern data architecture.

Understaffing and IT skills gap

Limited financial and human resources can constrain SMBs’ abilities to invest in advanced data management tools. These limitations often result in sub optimal practices for storing and managing data. At AWS, we know many SMBs lack in-house IT talent and it can make data management a struggle. The ensuing bottlenecks can make it challenging to access and analyze critical business information.

Data quality

Poor data quality is another common challenge for SMBs. Manual data entry, incomplete datasets, and data silos can degrade the quality of data, thereby skewing insights and decision-making. If your SMB struggles with various disconnected spreadsheets and legacy software, this should sound all too familiar. Similarly, maintaining data security and compliance is often a struggle for these businesses, as robust data security measures require significant resources.

Data integration

Lastly, integrating data from diverse sources, like social media, Customer Relationship Management (CRM) systems, and e-commerce platforms, can prove to be a major hurdle. These integration challenges often impede businesses from gaining a holistic understanding of their data, further hindering data-driven decision-making.

The importance of defining outcomes and prioritizing data challenges

AWS Connected Community CTA

Before delving into solutions, it is important to set clear data outcomes based on what the business is trying to achieve. This process involves identifying key performance indicators (KPIs) that reflect business goals and determining the data necessary to track these metrics. Most common examples of a key performance indicators would be revenue, profit margin, customer retention, and conversion rates. Also, understanding the current data landscape – what data the business collects, where it is stored, and how it is classified – can provide crucial insights into how well the business is poised to achieve its objectives.

However, data strategy does not stop at setting goals and understanding the data landscape; it is equally vital to prioritize data challenges. This process should include assessing the following:

  • Current data management situation
  • Identifying issues with data quality
  • Gaps in data collection
  • Storage problems

Moreover, determining which data sources are essential for decision-making and identifying patterns and trends in the data are crucial steps in this process.

Tackling data challenges with modern data architecture

To tackle these data challenges, SMBs can leverage modern data architecture patterns provided by AWS. This approach acknowledges the idea that taking a one-size-fits-all approach to analytics eventually leads to compromises. The core components of these patterns include scalable data lakes that can accommodate diverse data, purpose-built databases for addressing various data needs, serverless data integration service for seamless data movement, unified data governance, and various performance and cost optimization tools. These solutions can help SMBs manage their data more effectively and draw meaningful insights from it.

Aligning people, process, and technology

Diagram depicting the five elements of modern data architecture: data lakes, analytics, data movement, governance, and cost-effectiveveness

Figure 1: The elements of a modern data strategy

Creating a robust data strategy is not just about technology; it is also about aligning people and processes. Cultivating a data-centric culture within the organization, equipping the team with the necessary skills, and bringing in new talent, when necessary, can help optimize the use of data. There are several key roles that play a crucial part in the execution such as Chief Data Officer (executive sponsor), Data Stewards, Data Scientists/Engineers, and more. It’s worth noting that depending on the size of your SMB, an individual may function in multiple roles.

From a process perspective, automation can play a critical role in optimizing data ingestion, improving data quality, transformation, analysis, cost optimization, and governance. Also, technology choices should align with business needs.

AWS offers a wide range of technologies that address distinct aspects of data management. From scalable data lakes with services like Amazon Simple Storage Service (S3) to purpose-built analytics services like Amazon Athena, Amazon EMR, Amazon OpenSearch Service, Amazon Kinesis, and Amazon Redshift, there is a suite of tools designed to meet diverse needs. Seamless data movement with data quality measurement and monitoring can be achieved with AWS Glue while AWS Lake Formation can provide unified governance. Refer to Figure 1 to see how all of these services work together to achieve your technology goals.

Lastly, cost savings can be achieved with S3 intelligent tiering that saves up to 70 percent on data lake storage costs, and Amazon Elastic Computing Cloud that provides access to an industry-leading choice of over 500 instance types with various savings plans.

Measuring progress and adjusting the course

After setting up the data strategy and aligning the people, process, and technology, it is essential to measure the progress towards the set goals. Constant monitoring of KPIs will help assess the effectiveness of the data strategy and identify areas of improvement. Businesses should not shy away from adjusting their course if they find their current strategy is not delivering the expected results. Agile and flexible data strategies are often the most successful. We recommend setting up recurring meetings to ensure your stakeholders are informed of team wins and room for improvement.


In the world of SMBs, tackling data challenges can be a daunting task. However, by aligning their business vision with a well-crafted data strategy, understanding their data landscape, defining clear outcomes, and prioritizing data challenges, SMBs can pave the way to better data management and utilization.

Adopting modern data architecture patterns can help overcome many of these challenges. Furthermore, aligning people, process, and technology can maximize the effectiveness of a data strategy. By leveraging the services offered by AWS, SMBs can transform their data management and effectively turn their data into actionable insights.

In conclusion, the journey to overcome data challenges and unlock the true potential of data may be complex, but with the right strategy and tools, SMBs are well-equipped to navigate this journey successfully. To learn more about addressing your data challenges, contact an SMB expert at AWS.

John Walker

John Walker

John Walker is a Principal Solutions Architect at AWS supporting SMBs. He has over 20 years of technology architecture experience, leading companies of all industries to massive growth and rock-solid stability. John holds a degree in Computer Science from Texas A&M University and is based in Texas (US).

Dimple Dhar

Dimple Dhar

Dimple Dhar is a Sr. Solutions Architect with over 20 years of architecture, design, and development experience in multiple industries. She supports SMB customers at AWS with her strong engineering and analytical skills, as well as her ability to understand complex business processes. Dimple holds a degree from University of Jammu and is located in California (US).

Kunle Adeleke

Kunle Adeleke

Kunle Adeleke is a Sr. Solutions Architect, adept at designing and implementing effective technical and business solutions, who supports SMB customers at AWS. He has extensive experience in software design and solution, as well as the delivery of complex, multi-vendor, distributed systems in various industries such as financial services, automobile, law enforcement, government, and non-profit. He is based in Washington, D.C. (US).